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Main Authors: Wu, Kai, Hall, Brendan, Wang, Zhongqin, Zhang, Andrew, Guo, Jay
Format: Preprint
Published: 2026
Subjects:
Online Access:https://arxiv.org/abs/2605.24385
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author Wu, Kai
Hall, Brendan
Wang, Zhongqin
Zhang, Andrew
Guo, Jay
author_facet Wu, Kai
Hall, Brendan
Wang, Zhongqin
Zhang, Andrew
Guo, Jay
contents Digital audio broadcasting plus (DAB+) is an attractive illuminator for passive radar because it provides persistent, high-power, and geographically widespread very high frequency (VHF) orthogonal frequency-division multiplexing (OFDM) signals. A channel state information (CSI) sensing approach can convert a single received DAB+ stream into a CSI sequence for radar sensing, avoiding the need for a separately received reference signal in conventional passive radars. However, CSI estimation in DAB+ is challenging due to the differentially encoded communication symbols across time. A wrong symbol transition estimation leads to a persistent multiplicative error in the sequential CSI sequence within a DAB+ frame. This paper formulates single-stream DAB+ passive radar as a posterior-probability-aware differential CSI tracking problem. The proposed method uses the previously tracked CSI as a channel prior, performs prediction-aided maximum a posteriori detection of current symbol, converts posterior transition reliability into observation uncertainty, and applies linear minimum mean squared error fusion to obtain a stable tracking CSI. A reliability-informed CSI fusion strategy is also introduced to preserve weak target information. Theoretical analysis is provided, showing guaranteed performance again in symbol and CSI estimation. Simulation results show that the proposed method can reduce CSI estimation error by over 15~dB compared with prior art. It also improves median target-to-background ratio by more than 11~dB in random fading scenes. Experiments in Sydney, Australia demonstrate improved range-Doppler maps for commercial aircraft sensing.
format Preprint
id arxiv_https___arxiv_org_abs_2605_24385
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Posterior-Aware Differential Channel Tracking for Reliable Single-Stream DAB+ Passive Radar
Wu, Kai
Hall, Brendan
Wang, Zhongqin
Zhang, Andrew
Guo, Jay
Signal Processing
Digital audio broadcasting plus (DAB+) is an attractive illuminator for passive radar because it provides persistent, high-power, and geographically widespread very high frequency (VHF) orthogonal frequency-division multiplexing (OFDM) signals. A channel state information (CSI) sensing approach can convert a single received DAB+ stream into a CSI sequence for radar sensing, avoiding the need for a separately received reference signal in conventional passive radars. However, CSI estimation in DAB+ is challenging due to the differentially encoded communication symbols across time. A wrong symbol transition estimation leads to a persistent multiplicative error in the sequential CSI sequence within a DAB+ frame. This paper formulates single-stream DAB+ passive radar as a posterior-probability-aware differential CSI tracking problem. The proposed method uses the previously tracked CSI as a channel prior, performs prediction-aided maximum a posteriori detection of current symbol, converts posterior transition reliability into observation uncertainty, and applies linear minimum mean squared error fusion to obtain a stable tracking CSI. A reliability-informed CSI fusion strategy is also introduced to preserve weak target information. Theoretical analysis is provided, showing guaranteed performance again in symbol and CSI estimation. Simulation results show that the proposed method can reduce CSI estimation error by over 15~dB compared with prior art. It also improves median target-to-background ratio by more than 11~dB in random fading scenes. Experiments in Sydney, Australia demonstrate improved range-Doppler maps for commercial aircraft sensing.
title Posterior-Aware Differential Channel Tracking for Reliable Single-Stream DAB+ Passive Radar
topic Signal Processing
url https://arxiv.org/abs/2605.24385